Automated reservoir model prediction using ml/ai intergrating seismic, well log and production data

Patent metadata

Field	Value
Publication number	US-2022075915-A1
Application number	US-202017016075-A
Country	US
Kind code	A1
Filing date	Sep 9, 2020
Priority date	Sep 9, 2020
Publication date	Mar 10, 2022
Grant date	—

Abstract

Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

First claim

1 . A method comprising:
training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating the one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

2 . The method of claim 1 , further comprising:
training a second machine learning model to generate a dynamic reservoir 3D model based, at least in part, on the updated 3D model and dynamic modeling data, wherein the dynamic modeling data includes data used to predict flow properties of the subterranean reservoir.

3 . The method of claim 1 , further comprising:
applying seismic enhancement to the seismic data to provide enhanced seismic data, wherein the first machine learning model is based on the enhanced seismic data.

4 . The method of claim 3 , further comprising:
matching the well log data with the enhanced seismic data to produce matched well log data, wherein the first machine learning model is based on the matched well log data.

5 . The method of claim 1 , wherein the additional data is real-time data.

6 . The method of claim 1 , wherein the one or more integrated enhanced logs are machine learning generated logs of 2D properties of the subterranean reservoir.

7 . The method of claim 1 , further comprising preselecting at least one of relevant 2D seismic attributes, 3D seismic attributes, and 4D seismic attributes as the seismic data.

8 . One or more non-transitory machine-readable media comprising program code for generating one or more reservoir 3D models, the program code to:
train a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generate the one or more integrated enhanced logs from the first machine learning model; group the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; input additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and group the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

9 . The machine-readable media of claim 8 , further comprising program code to:
train a second machine learning model to generate a dynamic reservoir 3D model based, at least in part, on the updated 3D model and dynamic modeling data, wherein the dynamic modeling data includes data used to predict flow properties of the subterranean reservoir.

10 . The machine-readable media of claim 8 , further comprising program code to:
apply seismic enhancement to the seismic data to provide enhanced seismic data, wherein the first machine learning model is based on the enhanced seismic data.

11 . The machine-readable media of claim 10 , further comprising program code to:
match the well log data with the enhanced seismic data to produce matched well log data, wherein the first machine learning model is based on the matched well log data.

12 . The machine-readable media of claim 8 , wherein the additional data is real-time data.

13 . The machine-readable media of claim 8 , wherein the one or more integrated enhanced logs are machine learning generated logs of 2D properties of the subterranean reservoir.

14 . The machine-readable media of claim 8 , further comprising program code to:
preselect at least one of relevant 2D seismic attributes, 3D seismic attributes, and 4D seismic attributes as the seismic data.

15 . An apparatus comprising:
a processor; and a machine-readable medium having program code executable by the processor to cause the apparatus to,
train a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data;
generate the one or more integrated enhanced logs from the first machine learning model;
group the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir;
input additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and
group the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.

16 . The apparatus of claim 15 , further comprising program code to:
train a second machine learning model to generate a dynamic reservoir 3D model based, at least in part, on the updated 3D model and dynamic modeling data, wherein the dynamic modeling data includes data used to predict flow properties of the subterranean reservoir.

17 . The apparatus of claim 16 , further comprising a user interface, wherein at least one of the static reservoir 3D model, the updated 3D model, and the dynamic reservoir 3D model is visualized via the user interface.

18 . The apparatus of claim 15 , further comprising program code to:
apply seismic enhancement to the seismic data to provide enhanced seismic data, wherein the first machine learning model is based on the enhanced seismic data.

19 . The apparatus of claim 18 , further comprising program code to:
match the well log data with the enhanced seismic data to produce matched well log data, wherein the first machine learning model is based on the matched well log data.

20 . The apparatus of claim 15 , wherein the one or more integrated enhanced logs are machine learning generated logs of 2D properties of the subterranean reservoir.

Assignees

• Landmark Graphics Corp

Inventors

• Vallabhaneni Sridharan
• Roy Samiran
• Chaki Soumi
• Mandapaka Bhaskar Jogi Venkata
• Pakalapati Rajeev
• Srivastav Shreshth
• Priyadarshy Satyam

Classifications

• G01V1/282

  Application of seismic models, synthetic seismograms · CPC title

• G01V1/44

  using generators and receivers in the same well (G01V1/52 takes precedence) · CPC title

• G01V1/302 Primary

  in 3D data cubes · CPC title

• G01V2210/614

  Synthetically generated data · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title